First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA's constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System (CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily scale soil moisture into a land surface model (LSM). Our results, which are based on triple collocation analysis (TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale by past observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors of LSM soil moisture are improved by 61.3% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study is the first global assimilation of GNSS-based soil moisture observations in LSMs.

Automated summary

This study utilizes CYGNSS and SMAP satellite data to assimilate soil moisture observations, improving the accuracy of LSM global scale soil moisture estimates using TCA. However, assimilating satellite-based soil moisture in dense vegetation areas may degrade LSM performance.